Abstract
The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.
Highlights
Once the mechanical equipment or key components therein fail, the operation becomes abnormal, which leads to the collapse of the entire mechanical system, which in turn leads to serious economic losses or major disasters
Janssens et al [21] proposed a fault diagnosis method based on convolutional neural network (CNN), which extracts local feature information directly from the original vibration signal through a multi-layer convolution-pooling structure
By reinterpreting the ReLU and max layers as gate operations controlled by input x, convolutional neural networks can be understood as a bottom-up approach for selecting the useful information for decision making in the feedforward process by these gate operations and discarding those that contribute little to the decision
Summary
Once the mechanical equipment or key components therein fail, the operation becomes abnormal, which leads to the collapse of the entire mechanical system, which in turn leads to serious economic losses or major disasters. Janssens et al [21] proposed a fault diagnosis method based on convolutional neural network (CNN), which extracts local feature information directly from the original vibration signal through a multi-layer convolution-pooling structure. The feedback mechanism convolutional neural network is better than traditional convolutional neural networks in the diagnosis of rolling bearing faults These architectures do not effectively capture the high-level semantic concepts of fault diagnostic signals and obtain all the important feature information associated with them. This paper proposes a fault diagnosis algorithm for rolling bearings based on feedback mechanism convolutional neural network-sparse representation. By reinterpreting the ReLU and max layers as gate operations controlled by input x, convolutional neural networks can be understood as a bottom-up approach for selecting the useful information for decision making in the feedforward process by these gate operations and discarding those that contribute little to the decision This problem will give the solution ideas and specific processes in the following content
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